Baselining for compute resource allocation

ABSTRACT

Aspects of the disclosure provide for mechanisms for resource allocation in computer systems. A method of the disclosure may include running, on a plurality of nodes of a computer system, a benchmark workload utilizing a plurality of compute resources, and generating, by a processing device, a plurality of benchmarking results representing performance of the computer system utilizing the plurality of compute resources. The method may further include creating, by the processing device, a resource definition for the compute system in view of the benchmarking results, wherein the resource definition comprises a first resource allocation unit indicative of a first computing capacity of the computer system utilizing a first compute resource and a second resource allocation unit indicative of a second computing capacity of the computer system utilizing a second compute resource, and wherein the plurality of compute resources comprises the first compute resource and the second compute resource.

RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 15/889,143, filed Feb. 5, 2018, now U.S. Pat. No.10,613,961, which is herein incorporated by reference.

TECHNICAL FIELD

The implementations of the disclosure relate generally to computersystems and, more specifically, to baselining for resource allocation incomputer systems.

BACKGROUND

Cloud computing is a computing paradigm in which a customer pays a“cloud provider” to execute a program on computer hardware owned and/orcontrolled by the cloud provider. It is common for cloud providers tomake virtual machines hosted on its computer hardware available tocustomers for this purpose. The cloud provider typically provides aninterface that a customer can use to requisition virtual machines andassociated resources such as processors, storage, and network services,etc., as well as an interface a customer can use to install and executethe customer's program on the virtual machines that the customerrequisitions, together with additional software on which the customer'sprogram depends. For some such programs, this additional software caninclude such software components as a kernel and an operating system.Customers, that have installed and are executing their programs “in thecloud,” typically communicate with the executing program from remotegeographic locations using Internet protocols.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure is illustrated by way of examples, and not by way oflimitation, and may be more fully understood with references to thefollowing detailed description when considered in connection with thefigures, in which:

FIG. 1 is a block diagram of a network architecture in whichimplementations of the disclosure may operate;

FIG. 2 a block diagram of an example of a node according to animplementation of the disclosure;

FIG. 3 depicts a block diagram of a computer system operating inaccordance with one or more aspects of the present disclosure;

FIG. 4 is a flow diagram illustrating a method of baselining forresource allocation in a computer system according to an implementationof the disclosure;

FIG. 5 is a flow diagram illustrating a method of baselining forresource allocation in a computer system according to anotherimplementation of the disclosure;

FIG. 6 is a flow diagram illustrating a method of resource allocationaccording to an implementation of the disclosure; and

FIG. 7 illustrates a block diagram of one implementation of a computersystem.

DETAILED DESCRIPTION

Aspects of the disclosure provide for mechanisms for resource allocationin computer systems (e.g., a virtualized environment). Conventionalapproaches to resource allocation typically allocate compute resourcesto a user by quantities of compute resources. For example, centralprocessing units (CPU) resources may be allocated in units of cores. Asanother example, memory resources are allocated in units of bytes.

However, a heterogeneous computing environment may include variouscomputer systems (e.g., clouds) that possess different computingcapacities. These computer systems may use different resourcepresentations for tasks involving resource allocation (e.g., schedulingcontainers, application deployment, etc.). For example, some computersystems may allocate processing resources (e.g., CPU resources) by CPU(e.g., a CPU core) while other computer systems may allocate processingresources by vCPU (virtual central processing unit). However, theseresource presentations do not reveal consistent and meaningful computingcapacities of various computer systems. For example, the computingcapacities provided by different computer systems utilizing the sameamount of compute resource may vary given various models, vendors, clockrates, and other specifications of hardware of the computer systems. AvCPU on a first CPU may or may not deliver the same capacity as a vCPUon a second CPU. Furthermore, the computer systems may have varioustypes of hypervisors. This may pose different levels of performancepenalties across various components of the heterogeneous computingenvironment.

As such, end users of the heterogeneous computing environment do nothave clear and consistent views of how their workloads will perform ondifferent computer systems (e.g., clouds, host machines, etc.) utilizingspecific amounts of compute resources. This may cause resource overcommitment or under commitment and may cause unpredictable performanceof applications running in the heterogeneous computing environment.

Aspects of the present disclosure address the above deficiencies andother deficiencies of the conventional resource allocation techniques byproviding scheduling mechanisms (e.g., systems, methods,computer-readable medium, etc.) that can perform resource allocation inview of computing capacities of various computer systems. The schedulingmechanisms can perform baselining functions to determine the computingcapacities of various components of a heterogeneous computingenvironment (e.g., clouds). For example, the mechanisms can run abenchmark workload on a computer system of the heterogeneous computingenvironment to measure computing capacities of the computer systemutilizing various compute resources. The benchmark workload may beprovided by any suitable benchmarking standard that provides one or morecomputer benchmark specifications and computer programs for evaluatingperformance of a computer system (e.g., by determining a performancemeasure of the computer system). For example, the benchmarking standardmay provide a computer program and may define a benchmark specificationcorresponding to the computer program (e.g., a base runtime duration anda mechanism for determining a performance score in view of the baseruntime duration). The computer system may execute the computer program.Data of the execution of the computer program may be analyzed in view ofthe benchmark specification to determine a score or other performancemeasure of the computer system. For example, a runtime duration of theexecution of the computer program may be compared to the base runtimeduration to determine a score for the computer system. The benchmarkingstandard may include, for example, a STANDARD PERFORMANCE EVALUATIONCORPORATION™ (“SPEC”) benchmark (e.g., SPECint, SPECfp, etc.)

In one implementation, the scheduling mechanism can deploy one or morecontainers on each of a plurality of nodes of the computer system andcan run the benchmark workload in the containers utilizing a firstcompute resource (e.g., one CPU core, 1 gigabyte of memory, etc.). Insome embodiments, the scheduling mechanism can rerun the benchmarkworkload in the containers several times. The scheduling mechanism canalso monitor data about the containers (e.g., resource utilization,runtime durations, etc.) and generate a first plurality of benchmarkingresults in view of the monitored data. Each of the first plurality ofbenchmarking results may include a score representative of performanceof one or more of the containers during the execution of the benchmarkworkload. The first plurality of benchmarking results may be generatedin view of the benchmarking standard (e.g., SPECint, SPECfp, etc.). Thescheduling mechanism can then generate a resource definition in view ofthe first plurality of benchmarking results. For example, the schedulingmechanism can determine a first performance score in view of the firstplurality of benchmarking results (e.g., by determining an average valueof the first plurality of benchmarking results). As such, the firstperformance score indicates a computing capacity of the computer systemutilizing the first compute resource. The scheduling mechanism can thendefine the first performance score as a first resource allocation unitand associate the first resource allocation unit with the first computeresource. The scheduling mechanism can generate the resource definitionto indicate such association. A resource allocation unit as used hereincan refer to a compute resource of a computer system that can berequested, allocated, and/or consumed. The compute resource may beand/or include any type of resource that can be utilized by a componentof a computer system, such as a processing resource (e.g., CPUs, virtualprocessing units, etc.), a memory resource (e.g., physical memory,virtual memory, etc.), a storage resource (e.g., storage space availablein one or more data storage domains, host computer systems 120, etc.), anetwork resource (e.g., a network bandwidth), etc.

The scheduling mechanism can iteratively execute the benchmark workloadin the containers utilizing various compute resources and can generateadditional benchmarking results. For example, the scheduling mechanismcan execute the benchmark workload in the containers utilizing a secondcompute resource (e.g., 2 CPU cores, 2 gigabytes of memory) and cangenerate a second plurality of benchmarking results. Each of the secondplurality of benchmarking results may include a score indicative ofperformance of one or more of the containers utilizing the secondcompute resource. The scheduling mechanism can also determine a secondperformance score in view of the second plurality of benchmarkingresults (e.g., by determining an average value of the second pluralityof benchmarking results). As such, the second performance scoreindicates a computing capacity of the computer system utilizing thesecond compute resource. The scheduling mechanism can then update theresource definition to indicate an association between a second resourceallocation unit (e.g., the second performance score) and the secondcompute resource.

To request resource allocation in the heterogeneous computingenvironment, a user can submit a resource request including informationthat can be used to identify compute resources in view of the resourcedefinition. For example, the resource request can include a resourceallocation unit (e.g., the first resource allocation unit) correspondingto a performance score (e.g., the first performance score). The resourcerequest may also include identifying information of the benchmark thatwas used to generate the resource allocation unit. The schedulingmechanisms can allocate, in view of the resource definition, a computeresource (e.g., the first compute resource) corresponding to theresource allocation unit to fulfill the resource request.

In some embodiments, the scheduling mechanism can generate multipleresource definitions for various components (e.g., clouds, hostmachines, etc.) of the heterogeneous computing environment. For example,a first resource definition and a second resource definition may begenerated for a first cloud and a second cloud, respectively. In such anexample, different compute resources of different computer systems maybe allocated to fulfill the resource request described above. Forexample, the processing device can determine, in view of the firstresource definition, that a certain compute resource of the first cloud(e.g., a third compute resource) corresponds to the resource allocationunit and/or that the first cloud can provide the computing capacitycorresponding to the resource allocation unit utilizing the thirdcompute resource. The scheduling mechanism can also determine, in viewof the second resource definition, that a certain resource of the secondcloud (e.g., a fourth compute resource) corresponds to the resourceallocation unit and/or that the second cloud can provide the computingcapacity corresponding to the resource allocation unit utilizing thesecond compute resource. In some embodiments, the third compute resourceis different from the fourth compute resource. As such, the schedulingmechanism can allocate different compute resources of different computersystems to provide the same computing capacity. The processing devicecan thus provide consistent and predictable resource allocation in aheterogeneous environment including various resources provided byvarious resource providers (e.g., cloud providers).

Accordingly, aspects of the present disclosure provide for schedulingmechanisms that can perform resource allocation in view of computingcapacities of various computer systems. Unlike the conventional resourceallocation techniques that allocate resources in view of an absoluteamount of particular resource, the scheduling techniques disclosedherein can provide consistent computing capacities for users of aheterogeneous computing environment at large scale that span acrossmultiple clouds where types of hypervisors and hardware specificationsmay vary significantly. As a result, the scheduling mechanisms disclosedherein can allocate different compute resources of components of theheterogeneous computing environment to provide the same computingcapacity. The scheduling mechanisms can avoid over-committing orunder-committing resources of the heterogeneous computing environment.

FIG. 1 is a block diagram of a network architecture 100 in whichimplementations of the disclosure may operate. In some implementations,the network architecture 100 may be used in a Platform-as-a-Service(PaaS) system, such as OpenShift®. The PaaS system provides resourcesand services for the development and execution of applications owned ormanaged by multiple users. A PaaS system provides a platform andenvironment that allow users to build applications and services in aclustered compute environment (the “cloud”). Although implementations ofthe disclosure are described in accordance with a certain type ofsystem, this should not be considered as limiting the scope orusefulness of the features of the disclosure. For example, the featuresand techniques described herein can be used with other types ofmulti-tenant systems.

As shown in FIG. 1, the network architecture 100 may include acloud-computing environment 130 (also referred to herein as a cloud)that includes nodes 111, 112, 121, 122 to execute applications and/orprocesses associated with the applications. A “node” providing computingfunctionality may provide the execution environment for an applicationof the PaaS system. In some implementations, the “node” may refer to avirtual machine (VM) that is hosted on a physical machine, such as host1 110 through host N 120, implemented as part of the cloud 130. In someimplementations, the host machines 110, 120 are often located in a datacenter. For example, nodes 111 and 112 are hosted on physical machine110 in cloud 130 provided by cloud provider 104. In someimplementations, an environment other than a VM may be used to executefunctionality of the PaaS applications. When nodes 111, 112, 121, 122are implemented as VMs, they may be executed by operating systems (OSs)115, 125 on each host machine 110, 120.

In some implementations, the host machines 110, 120 may be located in adata center. Users can interact with applications executing on thecloud-based nodes 111, 112, 121, 122 using client computer systems, suchas clients 160, 170, and 180, via corresponding web browser applications161, 171, and 181. In other implementations, the applications may behosted directly on hosts 1 through N 110, 120 without the use of VMs(e.g., a “bare metal” implementation), and in such an implementation,the hosts themselves are referred to as “nodes”.

Clients 160, 170, and 180 are connected to hosts 110, 120 in cloud 130and the cloud provider system 104 via a network 102, which may be aprivate network (e.g., a local area network (LAN), a wide area network(WAN), intranet, or other similar private networks) or a public network(e.g., the Internet). Each client 160, 170, 180 may be a mobile device,a PDA, a laptop, a desktop computer, a tablet computing device, a serverdevice, or any other computing device. Each host 110, 120 may be aserver computer system, a desktop computer or any other computingdevice. The cloud provider system 104 may include one or more machinessuch as server computers, desktop computers, etc.

In one implementation, the cloud provider system 104 is coupled to acloud controller 108 via the network 102. The cloud controller 108 mayreside on one or more machines (e.g., server computers, desktopcomputers, etc.) and may manage the execution of applications incloud(s) 130. In some implementations, cloud controller 108 receivescommands from PaaS system controller 140. In view of these commands, thecloud controller 108 provides data (e.g., such as pre-generated images)associated with different applications to the cloud provider system 104.In some implementations, the data may be provided to the cloud provider104 and stored in an image repository 106, in an image repository (notshown) located on each host 110, 120, or in an image repository (notshown) located on each node 111, 112, 121, 122. This data may be usedfor the execution of applications for a multi-tenant PaaS system managedby the PaaS provider controller 140.

In one implementation, the data associated with the application mayinclude data used for execution of one or more containers that includeapplication images built from pre-existing application components andsource code of users managing the application. As used herein, an imagemay refer to data representing executables and files of the applicationused to deploy functionality for a runtime instance of the application.In one implementation, the image can be built using suitablecontainerization technologies (e.g., using a Docker tool).

In some embodiments, each of nodes 111, 112, 121, 122 can host one ormore containers. Each of the containers may be a secure process space onthe nodes 111, 112, 121 and 122 to execute functionality of anapplication and/or a service. In some implementations, a container isestablished at the nodes 111, 112, 121 and 122 with access to certainresources of the underlying node, including memory, storage, etc. In oneimplementation, the containers may be established using the LinuxContainers (LXC) method, cgroups, SELinux™, and kernel namespaces, etc.A container may serve as an interface between a host machine and asoftware application. The software application may comprise one or morerelated processes and may provide a certain service (e.g., an HTTPserver, a database server, etc.). Containerization is anoperating-system-level virtualization environment of a host machine thatprovides a way to isolate the micro-service processes. At the same time,employing the containers makes it possible to develop and deploy newcloud-based micro-services in a cloud-based system. In some embodiments,each node may be and/or include a node 200 of FIG. 2.

Each of nodes 111, 112, 121, 122 can host one or more applicationsand/or services. Each of the services may correspond to an applicationand/or one or more components of the application and may provide one ormore functionalities of the application. Examples of the services mayinclude a web server, a database server, a middleware server, etc. Eachof the services may run as an independent process in a suitable machine(e.g., a container, a virtual machine, a physical machine, etc.). Aservice running in system 100 may communicate with one or more otherservices in system 100. In one implementation, an application may bedeployed as one or more services on one or more nodes 111, 112, 121,122. Each of the services may be deployed in one or more containers. Insome implementations, a service may be an application.

Orchestration system 140 may provide management and orchestrationfunctions for deploying and/or managing applications and/or services insystem 100. For example, orchestration system 140 can build one or morecontainer images for providing functionality of the applications and/orthe services. The orchestration system 140 can then create one or morecontainers to host the application and/or service (e.g., by instructingone or more nodes 111, 112, 121, 122 to instantiate one or morecontainers from the container image(s)). Orchestration system 140 caninclude one or more computing devices (e.g., a computing device as shownin FIG. 7). Orchestration system 140 can implement an applicationprogramming interface (e.g., a Kubernetes API) to facilitate deployment,scaling, and/or management of containerized software applications.

In some embodiments, orchestration system 140 can deploy an applicationas one or more services on one or more nodes. Each of the services maybe deployed in one or more containers. In some embodiments, a replica ofthe service may be created by deploying one or more containers on a node(e.g., by deploying a pod as described in connection with FIG. 2 andstarting the pods to run the containers).

Orchestration system 140 can include a scheduler component 142 forscheduling deployments of applications and/or services on nodes 111,112, 121, 122 and/or to perform any other function for networkarchitecture 100. Scheduler component 142 can collect data about one ormore nodes 111, 112, 121, 122 for the deployments and/or otherfunctionality of orchestration system 140. The data may include, forexample, data about available compute resources on each of the nodes,data about utilization of compute resources on each of the nodes, dataabout allocation of compute resources to workloads on each of the nodes,data about containers and/or groups of container (e.g., pods asillustrated in FIG. 2) running on each of the nodes, etc. The data aboutthe containers and/or the groups of container may include, for example,data about resource utilization of the containers and/or the groups ofcontainers, such as data about processing resource usage, data aboutmemory resource usage, etc. In some embodiments, the scheduler component142 may collect the data by communicating with a scheduler agent (e.g.,a scheduler agent 222 of FIG. 2) of a respective node. In someembodiments, the scheduler agent may implement a Kubelet service.

In some embodiments, network architecture 100 can provide aheterogeneous computing environment utilizing various resources. Forexample, network architecture 100 can include multiple clouds 130provided by one or more providers. The clouds 130 may or may not providethe same computing capacity. For example, a component of a first cloud(e.g., a first node) and a component of a second cloud (e.g., a secondnode) may provide different computing capacities. As another example,the first cloud may provide a first computing capacity utilizing acertain compute resource (e.g., a certain processing resource, a certainmemory resource, etc.) while the second cloud may provide a secondcomputing capacity utilizing a certain compute resource. The firstcomputing capacity and the second capacity may be different in someembodiments. As such, allocating the same amount of compute resource inthe first cloud and the second cloud may provide different computingcapacities.

Instead of allocating resources in absolute amounts of the resources,the scheduler component 142 can perform resource allocation and/orscheduling tasks across the clouds in view of computing capacities ofvarious components of network architecture 100. For example, thescheduler component 142 can implement a baselining scheme to assesscomputing capacities of various components of network architecture 100utilizing various compute resources. Scheduler component 142 can obtainbenchmarking results indicative of computing capacities of thecomponents utilizing the compute resource. Scheduler component 142 canthen use the benchmarking results to determine basic resource allocationunits.

More particularly, for example, scheduler component 142 can execute abenchmark workload utilizing various compute resources of a computersystem (e.g., a cloud 130, a host machine, etc.) in network architecture100. The benchmark workload may be and/or include any computer programthat may be executed to measure a computing capacity of a computersystem or one or more components of the computer system. For example,the benchmark workload may include one or more computer programs formeasuring performance of integer arithmetic, memory operations,floating-point arithmetic, etc. of the computer system. The benchmarkworkload may be defined by a benchmarking standard that that providesone or more computer benchmark specifications and computer programs forevaluating performance of a computer system (e.g., by determining aperformance measure of the computer system). For example, thebenchmarking standard may provide a computer program and may define abenchmark specification (e.g., a base runtime duration and a mechanismfor determining a performance score in view of the base runtimeduration) corresponding to the computer program. The computer system mayexecute the computer program. Data of the execution of the computerprogram may be analyzed in view of the benchmark specification todetermine a score or other performance measure of the computer system.For example, a runtime duration of the execution of the computer programmay be compared to the base runtime duration to determine a score forthe computer system. The benchmarking standard may include, for example,be and/or include a STANDARD PERFORMANCE EVALUATION CORPORATION™(“SPEC”) specification (e.g., SPECint, SPECfp, etc.). The benchmarkworkload may also be defined by any other suitable benchmarkingstandard.

In some embodiments, scheduler component 142 can execute the benchmarkworkload utilizing a first compute resource (e.g., a CPU core, agigabyte of memory, etc.) on each of a plurality of nodes of thecomputer system. For example, scheduler component 142 can deploy one ormore containers on each of the nodes and run the benchmark workload inthe containers. As another example, scheduler component 142 can executethe benchmark workload on a virtual machine of each of the nodes.Scheduler component 142 can also execute the benchmark workload bycreating an instance of the benchmark workload on each of the nodes inany other suitable manner. Scheduler component 142 can generatebenchmarking results (a first plurality of benchmarking results)representing performance of the computer system during the execution ofthe benchmark workload utilizing the first compute resource. Each of thebenchmarking results may be and/or include a score representative of theperformance of the compute resource and can be generated by applying thebenchmark (e.g., SPECint, SPECfp, etc.). Scheduler component 142 canalso generate a first performance score in view of the firstbenchmarking results (e.g., by determining an average value of the firstbenchmarking results). The first performance score may represent thecomputing capacity of the computer system utilizing the first computeresource.

Scheduler component 142 can then increment the compute resource utilizedto execute the benchmark workload. For example, scheduler component 142can execute the benchmark workload in each of the nodes utilizing asecond compute resource. The second compute resource may be greater thanthe first compute resource. Scheduler component 142 can then generate asecond plurality of benchmarking results representing performance of thecomputer system utilizing the second compute resource. Schedulercomponent 142 can also determine a second performance score in view ofthe second plurality of benchmarking results. The second performancescore may represent the computing capacity of the computer systemutilizing the second compute resource.

In some embodiments, scheduler component 142 can iteratively incrementthe compute resource utilized to execute the benchmark workload forbaselining performance of the computer system as described above.Scheduler component 142 can then generate a resource definition for thecomputer system in view of benchmarking results corresponding to variouscompute resources (e.g., the first plurality of benchmarking results,the second plurality of benchmarking results, etc.). The resourcedefinition can include basic resource allocation units that can be usedfor resource allocation in the computer system. Each of the resourceallocation units may indicate a respective computing capacity. In oneimplementation, each of the resource allocation units may be aperformance score generated in view of one or more of the benchmarkingresults (e.g., the first performance score, the second performancescore, etc.). The resource definition can also include mapping data thatcan be used to map resource allocation units (e.g., performance scores)to compute resources of the computer system (e.g., data indicative of anassociation between the first performance score and the first computeresource). The mapping data may be stored using any suitable datastructure, such as one or more hash tables, search trees, lookup tables,etc.

In some embodiments, scheduler component 142 can generate a respectiveresource definition for each of a plurality of computer systems (e.g.,host machines, clouds, etc.). A given resource allocation unit (e.g., aperformance score) may correspond to various compute resources in thecomputer systems in some embodiments in which the computer systemsprovide different computing capacities utilizing the same computeresource. For example, a given performance score (e.g., a score of “10”)may correspond to a computing capacity that can be provided by a firstcloud utilizing a first compute resource or a second cloud utilizing asecond compute resource. In such an example, the given performance scoremay correspond to the first compute resource of the first cloud and thesecond compute resource of the second cloud.

To request resources in network architecture 100, a user can submit, viaa client (e.g., client 160, 170, 180), a resource request includinginformation that can be used to identify compute resources in view of aresource definition associated with a computer system. For example, theresource request can include a resource allocation unit (e.g., aperformance score of “10”) and identifying information of a benchmarkrelated to the resource allocation unit (e.g., the benchmark used togenerate the performance score). The resource request may be and/orinclude a request to run a workload (e.g., a computer program, anapplication, a service, etc.). In some embodiments, the user may requestto run the workload on any suitable computer system (e.g., a client) todetermine the resource allocation unit required to execute the workload.The resource request may be transmitted from the client to theorchestration system 140. Upon receiving the resource request, schedulercomponent 142 can identify a compute resource to fulfill the resourcerequest. For example, scheduler component 142 can convert theperformance score into the first compute resource of first cloud or thesecond compute resource of the second cloud. Scheduler component 142 canthen allocate the identified resource to the user to fulfill theresource request. For example, scheduler component 142 can allocate thefirst compute resource to one or more containers, virtual machines, etc.on the first cloud. As such, scheduler component 142 can performresource allocation in view of computing capacities of various clouds orany other computer systems. Unlike the conventional resource allocationtechniques that allocate in view of an absolute amount of particularresource, the scheduling techniques disclosed herein can provideconsistent computing capacities for users in a heterogeneous computingenvironment.

In some embodiments, the scheduler component 142 can include one or morecomponents described in connection with FIG. 3 and can implement one ormore methods described in connection with FIGS. 4-6.

While various implementations are described in terms of the environmentdescribed above, the facility may be implemented in a variety of otherenvironments including a single, monolithic computer system, as well asvarious other combinations of computer systems or similar devicesconnected in various ways. For example, the scheduler component 142and/or one or more portions of the scheduler component 142 may berunning on a node, such as nodes 111, 112, 121, 122, of the system 100hosted by cloud 130, or may execute external to cloud 130 on a separateserver device. In some implementations, the scheduler component 142 mayinclude more components that what is shown that operate in conjunctionwith the PaaS system of network 100.

FIG. 2 is a block diagram of an example 200 of a node according to animplementation of the disclosure. Node 200 can be a system providingrun-time environments for one or more containers. Node 200 may comprisea computing device with one or more processors communicatively coupledto memory devices and input/output (I/O) devices, as described in moredetails herein below with references to FIG. 7. Node 200 may include anoperating system (not shown in FIG. 2) with one or more user spaceprograms. The operating system may be any program or combination ofprograms that are capable of using the underlying computing device toperform computing tasks. The operating system may include a kernelcomprising one or more kernel space programs (e.g., memory driver,network driver, file system driver) for interacting with virtualhardware devices or actual hardware devices (e.g., para-virtualization).User space programs may include programs that are capable of beingexecuted by the operating system and in one example may be anapplication program for interacting with a user. Although node 200comprises a computing device, the term “node” may refer to the computingdevice (e.g., physical machine), a virtual machine, or a combinationthereof.

Node 200 may provide one or more levels of virtualization such ashardware level virtualization, operating system level virtualization,other virtualization, or a combination thereof. Node 200 may providehardware level virtualization by running a hypervisor that provideshardware resources to one or more virtual machines. The hypervisor maybe any program or combination of programs and may run on a hostoperating system or may run directly on the hardware (e.g., bare-metalhypervisor). The hypervisor may manage and monitor various aspects ofthe operation of the computing device, including the storage, memory,and network interfaces. The hypervisor may abstract the physical layerfeatures such as processors, memory, and I/O devices, and present thisabstraction as virtual devices to a virtual machine.

Node 200 (e.g., physical machine or virtual machine) may also oralternatively provide operating system level virtualization by running acomputer program that provides compute resources to one or morecontainers 224A-C. Operating system level virtualization may beimplemented within the kernel of operating system and may enable theexistence of multiple isolated containers. In one example, operatingsystem level virtualization may not require hardware support and mayimpose little to no overhead because programs within each of thecontainers may use the system calls of the same underlying operatingsystem. This enables node 200 to provide virtualization without the needto provide hardware emulation or be run in an intermediate virtualmachine as may occur with hardware level virtualization.

Operating system level virtualization may provide resource managementfeatures that isolate or limit the impact of one container (e.g.,container 224A) on the resources of another container (e.g., container224B or 224C). The operating system level virtualization may provide apool of resources that are accessible by container 224A and are isolatedfrom one or more other containers (e.g., container 224B). The pool ofresources may include file system resources (e.g., particular volume),network resources (e.g., particular network address), memory resources(e.g., particular memory portions), other compute resources, or acombination thereof. The operating system level virtualization may alsolimit a container's access to one or more compute resources bymonitoring the containers activity and restricting the activity in viewof one or more limits (e.g., quotas). The limits may restrict the rateof the activity, the aggregate amount of the activity, or a combinationthereof. The limits may include one or more of disk limits, input/out(I/O) limits, memory limits, CPU limits, network limits, other limits,or a combination thereof. In one example, an operating systemvirtualizer provides the compute resources to containers 224A-C. Theoperating system virtualizer may wrap an application in a complete filesystem that contains the code, runtime, system tools, system librariesand other programs installed on the node that can be used by theapplication. In one example, the operating system virtualizer may be thesame or similar to Docker for Linux®, ThinApp® by VMWare®, SolarisZones® by Oracle®, or other program that automates the packaging,deployment, and execution of applications inside containers.

Each of the containers 224A-C may refer to a resource-constrainedprocess space of node 200 that can execute functionality of a program.Containers 224A-C may be referred to as a user-space instances, avirtualization engines (VE), or jails and may appear to a user as astandalone instance of the user space of an operating system. Each ofthe containers 224A-C may share the same kernel but may be constrainedto only use a defined set of compute resources (e.g., CPU, memory, I/O).Aspects of the disclosure can create one or more containers to host aframework or provide other functionality of an application (e.g., proxyagent functionality, database functionality, web applicationfunctionality, etc.) and may therefore be referred to as “applicationcontainers.”

Pods 226A and 226B may be data structures that are used to organize oneor more containers 224A-C and enhance sharing between containers, whichmay reduce the level of isolation between containers within the samepod. Each pod may include one or more containers that share computeresources with another container associated with the pod. Each pod maybe associated with a unique identifier, which may be a networkingaddress (e.g., an IP address), that allows applications to use portswithout a risk of conflict. A pod may be associated with a pool ofresources and may define a volume, such as a local disk directory or anetwork disk and may expose the volume to one or more (e.g., all) of thecontainers within the pod. In one example, all of the containersassociated with a particular pod may be co-located on the same node 200.In another example, the containers associated with a particular pod maybe located on different nodes that are on the same or different physicalmachines. Node 200 can have any suitable number of pods. Each of thepods may have any suitable number of containers.

In some embodiments, node 200 may host one or more applications and/orservices. For example, node 200 can host service replicas for one ormore applications and/or services. Each of the services may correspondto an application. Each of the service replicas may be a copy of aservice and may run as an independent process in one or more containers224A-C. In one implementation, each of pods 226A and 226B may host arespective service replica. The service replicas hosted by pods 226A and226B may or may not correspond to the same service and/or application.

Node 200 can include a scheduler agent 222 that can create, start, stop,manage, etc. one or more of the containers and/or pods on node 200. Insome embodiments, scheduler agent can implement a Kubelet service.

Scheduler agent 222 can also monitor statuses of node 200, containers224A-C, and/or pods 226A-B and can transmit data about the statuses tothe scheduler component 142 of FIG. 2. A status of the node (alsoreferred to as the “node status”) may include any data about computeresources of the node and/or one or more components of the node (e.g., acontainer, a pod) in a certain period of time, data about an applicationor service running on the node (e.g., a runtime of the application),and/or any other data about the node. For example, the node status mayinclude data about resource usage by the node, one or more containersrunning on the node, one or more pods running on the node, such as anamount of a compute resource utilized by the node, the container(s), thepods, or the application during a certain period of time, a type of thecompute resource (e.g., a processing resource, a memory resource, etc.),etc. A status of a container (also referred to as the “containerstatus”) may include any data about compute resources associated withthe container (e.g., resource usage by the container in a certain timeperiod of time, an available compute resource, etc.), data about anapplication and/or service running in the container, and/or any otherdata about the container. A status of a pod (also referred to as the“pod status”) may include any data about compute resources associatedwith the pod (e.g., resource usage by the pod in a certain period oftime), data about an application and/or service running in the pod,and/or any other data about the pod. In some embodiments, the data aboutthe node status, the container status, and/or the pod status may betransmitted to the scheduler component 142 periodically, at random timeinstances, or in any other suitable interval. In some embodiments, thedata about the node status, the container status, and/or the pod statusmay be transmitted to the scheduler component 142 in response toreceiving a request for the data from the scheduler component 142.

Scheduler agent 222 can relay other information to and from thescheduler component 142 and/or any other component of orchestrationsystem 140. For example, scheduler agent 222 can receive commands,manifests, etc. for deploying and/or managing containers and/or pods onnode 200. The commands may include, for example, a command to deploy acontainer, a command to deploy a pod, a command to deploy a serviceand/or an application, a command to allocate a given compute resource toone or more containers, pods, or nodes, a command to execute a workloadutilizing the compute resource, etc. The manifests may include, forexample, a container manifest including properties of a container or apod (e.g., one or more container images, one or more containers to bedeployed, commands to execute on boot of the container(s), ports toenable upon the deployment of the container(s), etc.). In someembodiments, scheduler agent 222 can deploy a container and/or a pod onnode 200 in view of the commands, manifests, and/or any other dataprovided by scheduler system 140.

FIG. 3 depicts a block diagram of a computer system 300 operating inaccordance with one or more aspects of the present disclosure. Computersystem 300 may be the same or similar to orchestration system 140 ofFIG. 1 and may include one or more processing devices and one or morememory devices. In the example shown, computer system 300 may include aprocessing device comprising a benchmarking module 310, a monitoringmodule 320, a resource definition module 330, and a resource allocationmodule 340 and/or any other suitable component for implementing resourceallocation mechanisms and/or perform any other suitable function inaccordance with the present disclosure.

Benchmarking module 310 can perform benchmarking and/or baseliningfunctions to assess performance and/or computing capacities of one ormore computer systems (e.g., one or more components of networkarchitecture 100 of FIG. 1). In some embodiments, for each of thecomputer systems, benchmarking module 310 can assess computingcapacities of the computer system utilizing various compute resources.For example, benchmarking module 310 can execute a benchmark workload ona plurality of nodes of the computer system utilizing the variouscompute resources. In one implementation, benchmarking module 310 canexecute the benchmark workload on each of the nodes utilizing an initialcompute resource, such as a first compute resource comprising a firstprocessing resource (e.g., 1 CPU core), a first memory resource (e.g., 1gigabyte of memory), and/or any other suitable resource. Benchmarkingmodule 310 can generate first benchmarking results representingperformance of the computer system utilizing the first compute resource.Benchmarking module 310 can then iteratively increment the computeresource utilized to execute the benchmark workload to obtain additionalbenchmarking results representing performance of the computer systemutilizing additional resources. For example, benchmarking module 310 cangenerate second benchmarking results representing performance of thecomputer system utilizing a second compute resource.

Monitoring module 320 can monitor compute resources that can be utilizedby various components of a computer system (e.g., system 100, a hostcomputer system, a node, a container, a pod, etc.). Examples of thecompute resources (also referred to as the “resources”) may includeprocessing resources (e.g., CPUs, virtual processing units, etc.),memory resources (e.g., physical memory, virtual memory, etc.), storageresources (e.g., storage space available in one or more data storagedomains, host computer systems 120, etc.), network resources (e.g., anetwork bandwidth), etc.

In one example, monitoring module 320 may collect data related toresource usage in the computer system. In one example, the data mayinclude information about resources consumed by one or more componentsof the computer system (e.g., storage space, memory, network bandwidths,etc. consumed by one or more containers, groups of containers, VMs,hosts, etc.). In another example, the data may include informationrelated to input/output (I/O) operations (e.g., write operations, readoperations, etc.) performed by one or more components of the computersystem. In a further example, the data may include information relatedto compute resources that can be provided by one or more components ofthe computer system (e.g., storage space provided by one or more datastorage domains, host computer systems, etc.). Monitoring module 210 maycollect the data by polling a host, a node, a pod, or any othercomponent of the computer system for the data periodically, at randomintervals, responsive to an event (e.g., a user request for allocationresources to a VM), and/or in any other suitable manner.

In some embodiments, monitoring module 320 can monitor resource usageand/or performance of one or more components of the computer systemrunning a specific workload (e.g., a benchmark workload). For example,monitoring module 320 can collect data about resource utilization and/orruntime of an execution of the workload in a container, a group ofcontainers, a node, etc. of the computer system. The data may include,for example, a runtime duration of the execution, an amount of a computeresource consumed during the execution of the workload, etc.

Resource definition module 330 can process the benchmarking resultsprovided by benchmarking model 310 to create a resource definition. Forexample, resource definition module 330 can determine whether the firstplurality of benchmarking results satisfy a predefined condition. Thesatisfaction of the predefined condition may indicate that the computersystem can provide a predictable and consistent computing capacityutilizing the first compute resource. For example, the predefinedcondition may include that the first plurality of benchmarking resultsare within a predefined range. As another example, the predefinedcondition may include that a deviation of the first plurality ofbenchmarking results is not greater than a threshold value.

In response to determining that the predefined condition is notsatisfied, resource definition module 330 can determine that theallocation of the first compute resource is not to achieve predictableand consistent performance and can generate data to indicate suchdetermination.

Alternatively, resource definition module 320 can determine the resourcedefinition in view of the first plurality of benchmarking results inresponse to determining that the predefined condition is satisfied. Assuch, resource definition module 320 can avoid over commitment or undercommitment during resource allocation by using benchmarking results thatsatisfy the predefined condition to generate the resource definition. Insome embodiments, resource definition module 320 can determine aperformance score in view of the first plurality of benchmarkingresults. The performance score may be an average value of thebenchmarking results (e.g., a mean, median, mode, etc.), one of thebenchmarking results, an average value of a subset of the benchmarkingresults (e.g., a set of selected benchmarking results), and/or any othervalue that can represent the first plurality of benchmarking results.Resource definition module 320 can then associate the performance scorewith the first compute resource and can generate the resource definitionto indicate such association.

Resource allocation module 340 can allocate resources of the computersystem. In one implementation, resource allocation module 340 mayreceive, from a client device 101, a resource allocation request. Theresource request may be and/or include a request for allocation of aresource associated with a particular computing capacity (e.g., aperformance score generated utilizing a benchmark workload defined by abenchmarking standard). In one example, the request may be and/orinclude a request to create and/or deploy the virtual machine on acomputer system. In another example, the resource request may be and/orinclude a request to allocate additional resource to the virtual machinerunning on a host computer system. In still another example, theresource request may be and/or include a request to create a virtualdisk, a virtual network interface, and/or any other device using therequested resource.

The resource request may include information that can be used toidentify compute resources in view of a resource definition associatedwith a computer system. For example, the resource request can include aresource allocation unit (e.g., a performance score of “10”) andidentifying information of a benchmark related to the performance score(e.g., the benchmark used to generate the performance score). Uponreceiving the resource request, resource allocation module 340 canidentify a compute resource to fulfill the resource request. Forexample, resource allocation module 340 can convert the performancescore into a certain compute resource of the computer system associatedwith the performance score. Resource allocation module 340 can alsodetermine that the computer system can provide a computing capacitycorresponding to the performance score utilizing the identified computeresource. Resource allocation module 340 can then allocate theidentified resource to fulfill the resource request. For example,resource allocation module 340 can deploy one or more containers on thecomputer system to host an application and/or service for the serviceand can allocate the identified compute resource to the containers.

Computer system 300 can also include one or more memory devices storingbenchmarking results 352, resource definition 354, and image data 356.Benchmarking results 352 can include multiple sets of benchmarkingresults corresponding to various compute resources (e.g., the firstplurality of benchmarking results corresponding to the first computeresource, a second plurality of benchmarking results corresponding to asecond compute resource, etc.).

Resource definition 354 may include any data about the resourcedefinition generated as described above. Resource definition 354 caninclude mapping data that can be used to map performance scores. Themapping data may be stored using any suitable data structure that can beused to map performance scores to compute resources, such as one or morehash tables, search trees, lookup tables, etc.

The memory devices can also store image data 356 for deployment ofservices and/or applications in computer system 300. Image data 356 caninclude one or more images that may be any data structure for storingand organizing information that may be used by a node to provide acomputing service. The information within an image of image data 356 mayindicate the state of the image and may include executable information(e.g., machine code), configuration information (e.g., settings), orcontent information (e.g., file data, record data). Each of the imagesmay be capable of being loaded onto a node and may be executed toperform one or more computing tasks. Image data 356 may include one ormore container images, virtual machine images, disk images, otherimages, or a combination thereof. A container image may include a userspace program (e.g., application) along with a file system that containsthe executable code, runtime, system tools, system libraries and otherprograms to support the execution of the user space program on a node.In some embodiments, the container image does not include an operatingsystem but may be run by an operating system virtualizer that is part ofan existing operating system of the node. In some embodiments, acontainer image may be used to deploy a container on a node (e.g., bycreating and instantiating the container from the container image). Avirtual machine image may include both an operating system and one ormore user space programs. The virtual machine image may be loaded ontothe node and may be run by a hypervisor. A disk image may be the same orsimilar to a virtual machine image (e.g., virtual disk image) but may beloaded onto node 120 and run without using a hypervisor or other form ofvirtualization technology. In one example, an image may be generated bycreating a sector-by-sector copy of a source medium (e.g., hard drive ofexample machine). In another example, a disk image may be generatedbased on an existing image and may be manipulated before, during, orafter being loaded and executed. In some embodiments, a virtual machineimage and/or a disk image may be used to instantiate a service replicaor an application replica on a node.

FIG. 4 is a flow diagram illustrating a method 400 for baselining forcompute resource allocation according to an implementation of thedisclosure. Method 400 can be performed by processing logic that maycomprise hardware (e.g., circuitry, dedicated logic, programmable logic,microcode, etc.), software (such as instructions run on a processingdevice), firmware, or a combination thereof. In one implementation,method 400 is performed by a processing device (e.g. a processing device702 of FIG. 7) implementing a scheduler component as described inconnection with FIGS. 1-3.

Method 400 may begin at block 410 where the processing device canexecute a benchmark workload on a plurality of nodes of a computersystem utilizing various compute resources. In one implementation, theexecution of the benchmark workload may be initiated in response todetermining, by the processing device, that a resource definition hasnot been associated with the computer system. In another implementation,the execution of the benchmark workload may be initiated in response toreceiving a request for resource definition by the processing device.The request for resource definition may be originated from a clientdevice, a server (e.g., a server of a cloud provider system 104, anorchestration system 140, etc. of FIG. 1), and/or any other device. Thecomputer system may include, for example, one or more host machines,clouds, and/or any other component as described in connection withFIG. 1. In some embodiments, the computer system may be a cloud providedby a service provider.

The benchmark workload may be and/or include any computer program thatmay be executed to measure a computing capacity of a computer system orone or more components of the computer system. The benchmark workloadmay be defined by a benchmarking standard that provides one or morecomputer programs for accessing performance characteristics of acomputer system. For example, the benchmark may be and/or include a SPECbenchmark (e.g., SPECint, SPECfp, etc.). The benchmark workload may alsobe defined by any other suitable benchmark.

The benchmark workload may be executed on each of the nodes or each of asubset of the nodes (e.g., a plurality of selected nodes). For example,the processing device can create an instance of the benchmark workloadon each of the nodes. In one implementation, the benchmark workload maybe executed on each of a plurality of containers or groups of containers(e.g., pods) running on the nodes. In another implementation, thebenchmark workload may be executed in each of a plurality of virtualmachines running on the nodes.

The benchmark workload may be executed utilizing various computeresources of the computer system. For example, the processing device canexecute the benchmark workload in the computer system utilizing aninitial amount of compute resource, such as a first compute resourcecomprising a first processing resource (e.g., one CPU core), a firstmemory resource (e.g., 1 gigabytes of memory), and/or any other suitableresource. The processing device can then increment the initial amount ofcompute resource and can execute the benchmarking workload in thecomputer system utilizing more compute resources, resulting in a secondcompute resource that is greater than the first compute resource. Thesecond compute resource may be and/or include a second processingresource (e.g., two CPU cores), a second memory resource (e.g., 2gigabytes of memory), etc. In some embodiments, the processing devicecan iteratively increment the compute resource to be utilized to executethe benchmark workload in the computer system to identify a resourceallocation model for the computer system. For example, the processingdevice can identify multiple predetermined resource allocation units(e.g., performance scores) associated with the resource allocationmodel. The processing device can then identify the resource allocationmodel by identifying a compute resource as described above for each ofthe predetermined resource allocation units.

The benchmark workload may be executed on the nodes of the computersystem several times (e.g., a predetermined number of times) utilizing aparticular compute resource. For example, the processing device can runthe benchmark workload several times in each of the containers, thepods, etc. utilizing the first compute resource.

At block 420, the processing device can monitor statues of one or morecomponents of the computer system that execute the benchmark workload.For example, the processing device can monitor resource usage of one ormore components of the computer that execute the benchmark workload(e.g., one or more containers, pods, virtual machines, nodes, etc.). Theresource usage may be and/or include, for example, CPU usage, memoryusage, etc. of the components of the computer system. The statuses ofthe components (e.g., a node status, container status, pod status, etc.)may also include runtime durations of the executions of the benchmarkworkload by the components of the computer system.

At block 430, the processing device can generate benchmarking results inview of the statuses of the components of the computer system. Each ofthe benchmarking results may indicate performance of the computer systemor the components of the computer system running the benchmark workloadutilizing a given compute resource. For example, each of thebenchmarking results may be and/or include a performance scoredetermined by applying the benchmark.

The benchmarking results may correspond to executions of the benchmarkworkload utilizing the various compute resources. For example, thebenchmarking results may include multiple subsets of results, each ofwhich may correspond to a given compute resource. In one implementation,the benchmarking results may include a first plurality of benchmarkingresults representing performance of the computer system running thebenchmark workload utilizing the first compute resource. Thebenchmarking results may include a second plurality of benchmarkingresults representing performance of the computer system running thebenchmark workload utilizing the second compute resource.

At block 440, the processing device can create a resource definition forthe computer system in view of the benchmarking results. The resourcedefinition may include, for example, resource allocation units that canbe used for resource allocation in the computer system. The basicresource allocation unit may be a performance score generated in view ofthe benchmarking results. For example, a first performance score may begenerated in view of the first plurality of benchmarking results. Thefirst performance score may be, for example, an average value of thefirst plurality of benchmarking results. The processing device canassociate the first performance score with the first compute resourceand can generate the resource definition to indicate such association.Similarly, the processing device can determine a second performancescore in view of a second plurality of benchmarking results. Theprocessing device can generate the resource definition to indicate anassociation between the second performance score and the second computeresource.

FIG. 5 is a flow diagram illustrating a method 500 for baselining forresource allocation in a computer system according to anotherimplementation of the disclosure. Method 500 can be performed byprocessing logic that may comprise hardware (e.g., circuitry, dedicatedlogic, programmable logic, microcode, etc.), software (such asinstructions run on a processing device), firmware, or a combinationthereof. In one implementation, method 500 is performed by a processingdevice (e.g. a processing device 702 of FIG. 7) implementing a schedulercomponent as described in connection with FIGS. 1-3.

Referring to FIG. 5, method 500 may begin at block 510 where aprocessing device can deploy a plurality of containers in a computersystem. For example, one or more containers can be deployed on each of aplurality of nodes of the computer system. In some embodiments, a groupof containers (e.g., a pod) can be deployed on each node of the computersystem or each of a selected set of the nodes. In one implementation,each of the containers may be a copy of a template container. Forexample, each of the containers may be created in view of a containermanifest defining the template container. In some embodiments, each ofthe containers may be created in view of the same container image. Inanother implementation, the containers may be deployed as a plurality ofgroups of containers (e.g., pods) in the computer system. The processingdevice may also allocate certain compute resources to each of thecontainers (e.g., a processing resource, a memory resource, etc.). Eachof the groups of containers may be a copy of a template pod. Forexample, each of the groups of containers may be created in view of thesame container manifest that defines the template pod. The containermanifest defining the template pod may include one or more configurationfiles that define the template pod and/or each container in the templatepod. The configuration files may include one or more YAML files,JavaScript Object Notation (JSON) files, etc. The configuration filesmay include identifying information of one or more container images thatcan be used to deploy one or more containers in the group of containers.

At block 520, the processing device can allocate a first computeresource to execute a benchmark workload in the containers. The firstcompute resource may be and/or include a first processing resource(e.g., one CPU core), a first memory resource (e.g., 1G memory), and/orany other compute resource.

At block 530, the processing device can execute the benchmark workloadin the containers utilizing the allocated compute resource. In oneimplementation, the processing device can run the benchmark workload ineach of the containers. As another example, the processing device canrun the benchmark workload in each of a plurality of groups ofcontainers (e.g., each of a plurality of pods).

In some embodiments, the processing device can execute the benchmarkworkload multiple times (e.g., a threshold number of times). Forexample, the processing device can execute the benchmark workload in acertain number of containers or groups of containers. As anotherexample, the processing device can execute the benchmark workload ineach of the containers and/or groups of containers multiple times.

At block 540, the processing device can monitor statuses of thecontainers (e.g., container statuses). The container statuses may beand/or include data about resource utilization, runtime durations,and/or any other data related to the execution of the benchmark workloadin the containers. For example, the container statuses may include dataabout resource utilization, runtime durations, and/or any other dataabout an execution of the benchmark workload in a container or a groupof containers. The container statuses may include data about multipleexecutions of the benchmark workload in one or more containers or groupsof containers.

At block 550, the processing device can determine a plurality ofbenchmarking results in view of the container statuses. Each of thebenchmarking results may indicate performance of one or more of thecontainers and/or groups of containers (e.g., pods) that run thebenchmark workload utilizing the allocated compute resource. In someembodiments, a benchmarking result may be determined for each of thecontainers and/or groups of containers running the benchmark workloadapplying SPECint, SPECfp, and/or any other benchmark.

At block 560, the processing device can determine whether thebenchmarking results satisfy a predefined condition. The satisfaction ofthe predefined condition may indicate that the computer system mayachieve predictable and reliable performance (e.g., a particularperformance score using a particular benchmark) utilizing the allocatedcompute resource. For example, the predefined condition can be that thebenchmarking results fall in a predefined range. As another example, thepredefined condition may be that a deviation of the benchmarking results(e.g., a standard deviation, a squared deviation, a variance, etc.) isnot greater than a threshold.

In response to determining that the benchmarking results satisfy thepredefined condition, the processing device can proceed to block 570 andcan create a resource definition in view of the benchmarking results.For example, the processing device can determine a performance score inview of the benchmarking results. In a more particular example, theprocessing device can determine the performance score by calculating amean, median, mode, and/or any other value that can represent an averagevalue of the benchmarking results. In another more particular example,the processing device can designate one of the benchmarking results asbeing the performance score. In still another more particular example,the processing device can select one or more of the benchmarking resultsand can calculate the performance score in view of the selectedbenchmarking results (e.g., by determining a mean, median, mode, etc. ofthe selected benchmarking results).

The processing device can then generate a resource allocation unitincluding the performance score and associate the resource allocationunit with the allocated resource. The processing device can furthergenerate the resource definition to indicate such association. Theresource definition may also include information about the benchmarkingworkload, such as identifying information of a benchmarking standardthat defines the benchmark workload (e.g., SPECint, SPECfp).

At block 580, the processing device can determine whether a resourceallocation model is identified for the computer system. For example, theprocessing device can determine that the resource allocation model isidentified in response to determining that a compute resource has beenidentified to provide a computing capacity corresponding to each of aplurality of predetermined resource allocation units (e.g., one or morepredetermined performance scores) associated with the resourceallocation model. The processing device can also determine that theresource allocation model is to be identified in response to determiningthat that processing device is to identify a compute resource that canprovide a computing capacity corresponding to at least one of thepredetermined resource allocation units.

In some embodiments, the processing device can proceed to block 590 inresponse to determining that the benchmarking results do not satisfy thepredefined condition (e.g., “NO” at block 560) and/or that the resourceallocation model is to be identified (e.g., “NO” at block 580″). Atblock 590, the processing device can allocate an additional computeresource to execute the benchmark workload in the containers. Theadditional compute resource may be and/or include any type of resources,such as a processing resource, a memory resource, etc. For example, theprocessing device can allocate a second compute resource to execute thebenchmark workload in the containers. The second compute resource may beand/or include, for example, a second processing resource (e.g., two CPUcores), a second memory resource (e.g., 2 gigabytes of memory), a secondnetworking resource, etc. The second compute resource may be greaterthan the first compute resource. For example, the second processingresource may be greater than the first processing resource. As anotherexample, the second memory resource may be greater than the first memoryresource.

The processing device can then loop back to block 530 and can executethe benchmark workload utilizing the allocated resource. For example,the processing device can execute the benchmark workload in each of thecontainers and/or each of groups of the containers utilizing the secondcompute resource. The processing device can generate a second pluralityof second benchmarking results as described above. Each of the secondplurality of benchmarking results may represent performance of one ofthe containers or one of the groups of containers utilizing the secondcompute resource during the execution of the benchmark workload. Theprocessing device can also generate a second performance score in viewof the second plurality of benchmarking results. The processing devicecan then update the resource definition to indicate an associationbetween the second performance and the second compute resource.

In some embodiments, in response to determining that the resourceallocation model is identified at block 580, the processing device canconclude process 500.

FIG. 6 is a flow diagram illustrating a method 600 for resourceallocation in a computer system according to an implementation of thedisclosure. Method 600 can be performed by processing logic that maycomprise hardware (e.g., circuitry, dedicated logic, programmable logic,microcode, etc.), software (such as instructions run on a processingdevice), firmware, or a combination thereof. In one implementation,method 600 is performed by a processing device (e.g. a processing device702 of FIG. 7) implementing a scheduler component as described inconnection with FIGS. 1-3.

Referring to FIG. 6, method 600 may begin at block 610 where aprocessing device can receive a resource request related to a computeresource. The resource request can be and/or include a request to deploya service, a request to deploy an application, a request to deploy acontainer, a request to deploy a pod, a request to execute a workload,and/or any other request including a request for the compute resource.

The resource request may include any suitable information related to thecompute resource. For example, the resource request can includeinformation that can be used to identify a certain compute resource inview of a resource definition. More particularly, for example, theresource request may include a resource allocation unit indicative of acomputing capacity. The resource allocation unit may include aperformance score generated in view of a benchmark. The resource requestmay also include information about the benchmark, such as identifyinginformation that can be used to identify the benchmark (e.g., adescription of the benchmark, a name of the benchmark, a version numberof the benchmark, etc.). The performance score and/or the resourceallocation unit may be generated using the benchmark as described inconnection with FIGS. 4 and/or 5 above.

At block 620, the processing device can identify, in view of theresource request and a resource definition, a compute resource of acomputer system that can fulfill the request. The resource definitionmay be generated by performing one or more operations as described inconnection with FIGS. 4-5 above. The resource definition may include,for example, mapping information that can be used to map resourceallocation units (e.g., performance scores) to compute resources. Theprocessing device can convert the resource allocation unit into acompute resource of a computer system that can be utilized by thecomputer system to provide the computing capacity corresponding to theperformance score related to the resource allocation unit.

In some embodiments, different compute resources of different computersystems may be allocated to fulfill the resource request. For example,the processing device can determine, in view of a first resourcedefinition associated with a first computer system, that a first computeresource of the first computer system corresponds to the performancescore. The processing device can further determine that the firstcomputer system is capable of providing the computing capacitycorresponding to the performance score utilizing the first computeresource. As another example, the processing device can determine, inview of a second resource definition associated with a second computersystem, that a second compute resource of the second computer systemcorresponds to the performance score. The processing device can furtherdetermine that the second computer system is capable of providing thecomputing capacity corresponding to the performance score utilizing thesecond compute resource. The second compute resource may or may not bethe same as the first compute resource. In some embodiments, the secondcompute resource is different from the first compute resource. As such,the processing device can allocate different compute resources ofdifferent computer systems to provide the same computing capacitycorresponding to the performance score in the resource request. Theprocessing device can thus provide consistent and predictable resourceallocation in a heterogeneous environment including various resourcesprovided by various resource providers (e.g., cloud providers).

At block 630, the processing device can allocate the compute resource ofthe computer system to fulfill the resource request. For example, theprocessing device can deploy a container on a node of the computersystem and can allocate the compute resource to the container. Asanother example, the processing device can allocate the compute resourceto a virtual machine running in the computer system.

FIG. 7 illustrates a diagrammatic representation of a machine in theexample form of a computer system 400 within which a set ofinstructions, for causing the machine to perform any one or more of themethodologies discussed herein, may be executed. In alternativeembodiments, the machine may be connected (e.g., networked) to othermachines in a LAN, an intranet, an extranet, or the Internet. Themachine may operate in the capacity of a server or a client device in aclient-server network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine may be apersonal computer (PC), a tablet PC, a set-top box (STB), a PersonalDigital Assistant (PDA), a cellular telephone, a web appliance, aserver, a network router, switch or bridge, or any machine capable ofexecuting a set of instructions (sequential or otherwise) that specifyactions to be taken by that machine. Further, while a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute a set (ormultiple sets) of instructions to perform any one or more of themethodologies discussed herein.

The computer system 700 includes a processing device 702 (e.g.,processor, CPU, etc.), a main memory 704 (e.g., read-only memory (ROM),flash memory, dynamic random access memory (DRAM) (such as synchronousDRAM (SDRAM) or DRAM (RDRAM), etc.), a static memory 706 (e.g., flashmemory, static random access memory (SRAM), etc.), and a data storagedevice 718, which communicate with each other via a bus 708.

Processing device 702 represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device may be complex instruction setcomputing (CISC) microprocessor, reduced instruction set computer (RISC)microprocessor, very long instruction word (VLIW) microprocessor, orprocessor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processing device 702may also be one or more special-purpose processing devices such as anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), a digital signal processor (DSP), network processor,or the like. The processing device 702 is configured to execute theprocessing logic 726 for performing the operations and steps discussedherein.

The computer system 400 may further include a network interface device722 communicably coupled to a network 764. The computer system 400 alsomay include a video display unit 710 (e.g., a liquid crystal display(LCD) or a cathode ray tube (CRT)), an alphanumeric input device 712(e.g., a keyboard), a cursor control device 714 (e.g., a mouse), and asignal generation device 720 (e.g., a speaker).

The data storage device 718 may include a machine-accessible storagemedium 724 on which is stored software 726 embodying any one or more ofthe methodologies of functions described herein. The software 726 mayalso reside, completely or at least partially, within the main memory704 as instructions 726 and/or within the processing device 702 asprocessing logic 726 during execution thereof by the computer system400; the main memory 704 and the processing device 702 also constitutingmachine-accessible storage media.

The machine-readable storage medium 724 may also be used to storeinstructions 726 to manage thread pools, such as the scheduler component142 as described with respect to FIGS. 1-3, and/or a software librarycontaining methods that call the above applications. While themachine-accessible storage medium 724 is shown in an example embodimentto be a single medium, the term “machine-accessible storage medium”should be taken to include a single medium or multiple media (e.g., acentralized or distributed database, and/or associated caches andservers) that store the one or more sets of instructions. The term“machine-accessible storage medium” shall also be taken to include anymedium that is capable of storing, encoding or carrying a set ofinstruction for execution by the machine and that cause the machine toperform any one or more of the methodologies of the disclosure. The term“machine-accessible storage medium” shall accordingly be taken toinclude, but not be limited to, solid-state memories, and optical andmagnetic media.

Other computer system designs and configurations may also be suitable toimplement the system and methods described herein. The followingexamples illustrate various implementations in accordance with one ormore aspects of the present disclosure.

Example 1 is a method comprising: deploying a plurality of containers ona plurality of nodes of a computer system; executing, in the pluralityof containers, a benchmark workload utilizing a first compute resource;obtaining a first plurality of benchmarking results representingperformance of the containers utilizing the first compute resource; andcreating, by a processing device, a resource definition in view of thefirst plurality of benchmarking results, wherein the resource definitioncomprises a first resource allocation unit indicative of a firstcomputing capacity of the computer system utilizing the first computeresource and an association between the first resource allocation unitand the first compute resource.

Example 2 is the method of example 1, wherein the resource definitionfurther comprises identifying information of a benchmark that definesthe benchmark workload.

Example 3 is the method of example 1, wherein creating the resourcedefinition comprises: in response to determining that the firstplurality of benchmarking results satisfies a predefined condition,determining a first performance score in view of the first plurality ofbenchmarking results, wherein the first resource allocation unitcomprises the first performance score.

Example 4 is the method of example 3, wherein determining the firstperformance score in view of the first plurality of benchmarking resultscomprises determining an average value of the first plurality ofbenchmarking results.

Example 5 is the method of example 3, wherein the predefined conditioncomprises at least one of that the first plurality of benchmarkingresults falls within a predefined range or that a deviation of the firstplurality of benchmarking results is not greater than a threshold.

Example 6 is the method of example 1, further comprising receiving arequest for resource allocation comprising the first resource allocationunit; determining, in view of the resource definition, that the computersystem provides the first computing capacity utilizing the first computeresource; and allocating the first compute resource to fulfill therequest for resource allocation.

Example 7 is the method of example 1, further comprising: executing, inthe plurality of containers, the benchmark workload utilizing a secondcompute resource; obtaining a second plurality of benchmarking resultsrepresenting performance of the containers utilizing the second computeresource; and creating the resource definition in view of the secondplurality of benchmarking results, wherein the resource definitioncomprises a second resource allocation unit indicative of a secondcomputing capacity of the computer system utilizing the second computeresource and an association between the second performance score and thesecond compute resource.

Example 8 is the method of example 7, wherein the first compute resourcecomprises a first processing resource, and wherein the second computeresource comprises a second processing resource.

Example 9 is the method of example 7, wherein the first compute resourcecomprises a first memory resource, and wherein the second computeresource comprises a second memory resource.

Example 10 is the method of example 1, further comprising monitoringresource utilization of the containers and runtime durations ofexecutions of the benchmark workload in the containers to generate thefirst plurality of benchmarking results.

Example 11 is a method comprising: running, on a plurality of nodes of acomputer system, a benchmark workload utilizing a plurality of computeresources; generating, by a processing device, a plurality ofbenchmarking results representing performance of the computer systemutilizing the plurality of compute resources; and creating, by theprocessing device, a resource definition for the compute system in viewof the benchmarking results, wherein the resource definition comprises afirst resource allocation unit indicative of a first computing capacityof the computer system utilizing a first compute resource and a secondresource allocation unit indicative of a second computing capacity ofthe computer system utilizing a second compute resource, and wherein theplurality of compute resources comprises the first compute resource andthe second compute resource.

Example 12 is the method of example 11, wherein the resource definitionfurther comprises mapping data indicative of a first association betweenthe first resource allocation unit and the first compute resource and asecond association between the second resource allocation unit and thesecond compute resource.

Example 13 is the method of example 11, wherein the resource definitionfurther comprises identifying information of a benchmark related to thebenchmark workload.

Example 14 is the method of example 11, wherein generating the pluralityof benchmarking results comprises generating a first plurality ofbenchmarking results representing performance of the computer systemutilizing the first compute resource.

Example 15 is the method of example 11, further comprising: in responseto determining that the first plurality of benchmarking results fallwithin a predefined range, determining a first performance score in viewof the first plurality of benchmarking results, wherein the firstresource allocation unit comprises the first performance score.

Example 16 is the method of example 11, wherein the first computeresource comprises a first processing resource, and wherein the secondcompute resource comprises a second processing resource.

Example 17 is the method of example 11, wherein the first computeresource comprises a first memory resource, and wherein the secondcompute resource comprises a second memory resource.

Example 18 is the method of example 11, further comprising: deploying aplurality of containers on the plurality of computer nodes, whereinrunning, on the plurality of nodes of the computer system, the benchmarkworkload utilizing the plurality of compute resources comprisesexecuting the benchmark workload in each of the plurality of containersutilizing the plurality of compute resources.

Example 19 is the method of example 11, wherein running, on theplurality of nodes of the computer system, the benchmark workloadutilizing the plurality of compute resources comprises executing thebenchmark workload on a plurality of virtual machines utilizing theplurality of compute resources.

Example 20 is the method of example 11, further comprising: monitoringresource utilization of the nodes during the execution of the benchmarkworkload to generate execution data; and obtaining the plurality ofbenchmarking results in view of the execution data.

Example 21 is the method of example 11, further comprising: receiving arequest for resource allocation comprising the first resource allocationunit; determining, in view of the resource definition, that the computersystem can provide the first computing capacity utilizing the firstcompute resource; and allocating the first compute resource to fulfillthe request for resource allocation.

Example 22 is a method comprising: receiving a request for resourceallocation comprising a resource allocation unit indicative of acomputing capacity; determining, by a processing device, a first computeresource of a first computer system in view of a first resourcedefinition related to the first computer system, wherein the firstresource definition comprises data indicative of an association betweenthe first compute resource and the resource allocation unit; andallocating the first compute resource to fulfill the request forresource allocation.

Example 23 is the method of example 22, wherein the resource allocationunit comprises a performance score generated in view of a benchmark, andwherein the resource request further comprises identifying informationof the benchmark.

Example 24 is the method of example 22, wherein determining the firstcompute resource comprises: determining, in view of the first resourcedefinition, that the first computer system provides the computingcapacity utilizing the first compute resource.

Example 25 is the method of example 22, wherein the first computersystem is a virtualized computer system.

Example 26 is the method of example 22, wherein allocating the firstcompute resource comprises: deploying a container on a node of the firstcomputer system; and allocating the first compute resource to thecontainer.

Example 27 is the method of example 22, wherein allocating the firstcompute resource comprises: allocating the first compute resource to avirtual machine running on the computer system.

Example 28 is the method of example 22, further comprising: identifying,by the processing device, a second compute resource of a second computersystem in view of the resource allocation unit and a second resourcedefinition associated with the second computer system, wherein thesecond resource definition comprises data indicative of an associationbetween the second compute resource and the resource allocation unit;and allocating the second compute resource to fulfill the request forresource allocation.

Example 29 is the method of example 28, wherein the first computeresource comprises a first processing resource, and wherein the secondcompute resource comprises a second processing resource.

Example 30 is the method of example 28, wherein the first computeresource comprises a first memory resource, and wherein the secondcompute resource comprises a second memory resource.

Example 31 is the method of example 30, wherein identifying the secondcompute resource comprises: determining, in view of the second resourcedefinition, that the second computer system provides the computingcapacity utilizing the second compute resource.

Example 32 is an apparatus comprising: a processing device; and meansfor running, on a plurality of nodes of a computer system, a benchmarkworkload utilizing a plurality of compute resources; means forgenerating a plurality of benchmarking results representing performanceof the computer system utilizing the plurality of compute resources; andmeans for creating a resource definition for the compute system in viewof the benchmarking results, wherein the resource definition comprises afirst resource allocation unit indicative of a first computing capacityof the computer system utilizing a first compute resource and a secondresource allocation unit indicative of a second computing capacity ofthe computer system utilizing a second compute resource, and wherein theplurality of compute resources comprises the first compute resource andthe second compute resource.

Example 33 is the apparatus of example, further comprising the subjectmatter of any of examples 1-31.

Example 34 is a system comprising a memory and a processing deviceoperatively coupled to the memory, the processing device to implementthe method of any of examples 1-31.

Example 35 is a non-transitory machine-readable storage medium includinginstructions that, when accessed by a processing device, cause theprocessing device to implement the method of any of examples 1-31.

In the foregoing description, numerous details are set forth. It will beapparent, however, that the disclosure may be practiced without thesespecific details. In some instances, well-known structures and devicesare shown in block diagram form, rather than in detail, in order toavoid obscuring the disclosure.

Some portions of the detailed descriptions which follow are presented interms of algorithms and symbolic representations of operations on databits within a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise, as apparent from the followingdiscussion, it is appreciated that throughout the description,discussions utilizing terms such as “sending,” “receiving,” “creating,”“assigning,” “providing,” “executing,” “determining,” “copying,”“storing,” “identifying,” “gathering,” “allocating,” “associating,”“identifying,” “running,” or the like, refer to the action and processesof a computer system, or similar electronic computing device, thatmanipulates and transforms data represented as physical (electronic)quantities within the computer system's registers and memories intoother data similarly represented as physical quantities within thecomputer system memories or registers or other such information storage,transmission or display devices.

The terms “first,” “second,” “third,” “fourth,” etc. as used herein aremeant as labels to distinguish among different elements and may notnecessarily have an ordinal meaning according to their numericaldesignation.

The disclosure also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for thepurposes, or it may comprise a general purpose computer selectivelyactivated or reconfigured by a computer program stored in the computer.Such a computer program may be stored in a machine readable storagemedium, such as, but not limited to, any type of disk including floppydisks, optical disks, CD-ROMs, and magnetic-optical disks, read-onlymemories (ROMs), random access memories (RAMs), EPROMs, EEPROMs,magnetic or optical cards, or any type of media suitable for storingelectronic instructions, each coupled to a computer system bus.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the method steps. The structure for a variety ofthese systems will appear as set forth in the description below. Inaddition, the disclosure is not described with reference to anyparticular programming language. It will be appreciated that a varietyof programming languages may be used to implement the teachings of thedisclosure as described herein.

The disclosure may be provided as a computer program product, orsoftware, that may include a machine-readable medium having storedthereon instructions, which may be used to program a computer system (orother electronic devices) to perform a process according to thedisclosure. A machine-readable medium includes any mechanism for storingor transmitting information in a form readable by a machine (e.g., acomputer). For example, a machine-readable (e.g., computer-readable)medium includes a machine (e.g., a computer) readable storage medium(e.g., read only memory (“ROM”), random access memory (“RAM”), magneticdisk storage media, optical storage media, flash memory devices, etc.),etc.

The above description is intended to be illustrative, and notrestrictive. Although the present disclosure has been described withreferences to specific illustrative examples and implementations, itwill be recognized that the present disclosure is not limited to theexamples and implementations described. The scope of the disclosureshould be determined with reference to the following claims, along withthe full scope of equivalents to which the claims are entitled.

What is claimed is:
 1. A method comprising: running, on a plurality ofnodes of a computer system, a benchmark workload utilizing a pluralityof compute resources; generating, by a processing device, a plurality ofbenchmarking results representing performance of the computer systemutilizing the plurality of compute resources; and creating, by theprocessing device, a resource definition for the computer system in viewof the plurality of benchmarking results, wherein the resourcedefinition comprises a first resource allocation unit indicative of afirst computing capacity of the computer system utilizing a firstcompute resource and a second resource allocation unit indicative of asecond computing capacity of the computer system utilizing a secondcompute resource, and wherein the plurality of compute resourcescomprises the first compute resource and the second compute resource. 2.The method of claim 1, wherein the resource definition further comprisesmapping data indicative of a first association between the firstresource allocation unit and the first compute resource and a secondassociation between the second resource allocation unit and the secondcompute resource.
 3. The method of claim 1, wherein the resourcedefinition further comprises identifying information of a benchmarkrelated to the benchmark workload.
 4. The method of claim 3, whereingenerating the plurality of benchmarking results comprises generating afirst plurality of benchmarking results representing performance of thecomputer system utilizing the first compute resource.
 5. The method ofclaim 4, further comprising: in response to determining that the firstplurality of benchmarking results fall within a predefined range,determining a first performance score in view of the first plurality ofbenchmarking results, wherein the first resource allocation unitcomprises the first performance score.
 6. The method of claim 1, whereinthe first compute resource comprises a first processing resource, andwherein the second compute resource comprises a second processingresource.
 7. The method of claim 1, wherein the first compute resourcecomprises a first memory resource, and wherein the second computeresource comprises a second memory resource.
 8. The method of claim 1,further comprising: deploying a plurality of containers on the pluralityof computer nodes, wherein running, on the plurality of nodes of thecomputer system, the benchmark workload utilizing the plurality ofcompute resources comprises executing the benchmark workload in each ofthe plurality of containers utilizing the plurality of computeresources.
 9. The method of claim 1, wherein running, on the pluralityof nodes of the computer system, the benchmark workload utilizing theplurality of compute resources comprises executing the benchmarkworkload on a plurality of virtual machines utilizing the plurality ofcompute resources.
 10. The method of claim 1, further comprising:monitoring resource utilization of the nodes during the execution of thebenchmark workload to generate execution data; and obtaining theplurality of benchmarking results in view of the execution data.
 11. Anon-transitory machine-readable storage medium including instructionsthat, when accessed by a processing device, cause the processing deviceto: run, on a plurality of nodes of a computer system, a benchmarkworkload utilizing a plurality of compute resources; generate aplurality of benchmarking results representing performance of thecomputer system utilizing the plurality of compute resources; and createa resource definition for the computer system in view of the pluralityof benchmarking results, wherein the resource definition comprises afirst resource allocation unit indicative of a first computing capacityof the computer system utilizing a first compute resource and a secondresource allocation unit indicative of a second computing capacity ofthe computer system utilizing a second compute resource, and wherein theplurality of compute resources comprises the first compute resource andthe second compute resource.
 12. The non-transitory machine-readablestorage medium of claim 11, wherein the resource definition furthercomprises mapping data indicative of a first association between thefirst resource allocation unit and the first compute resource and asecond association between the second resource allocation unit and thesecond compute resource.
 13. A system comprising: a memory; and aprocessing device, coupled to the memory to: run, on a plurality ofnodes of a computer system, a benchmark workload utilizing a pluralityof compute resources; generate a plurality of benchmarking resultsrepresenting performance of the computer system utilizing the pluralityof compute resources; and create a resource definition for the computersystem in view of the plurality of benchmarking results, wherein theresource definition comprises a first resource allocation unitindicative of a first computing capacity of the computer systemutilizing a first compute resource and a second resource allocation unitindicative of a second computing capacity of the computer systemutilizing a second compute resource, and wherein the plurality ofcompute resources comprises the first compute resource and the secondcompute resource.
 14. The system of claim 13, wherein the resourcedefinition further comprises mapping data indicative of a firstassociation between the first resource allocation unit and the firstcompute resource and a second association between the second resourceallocation unit and the second compute resource.
 15. The system of claim13, wherein the resource definition further comprises identifyinginformation of a benchmark related to the benchmark workload.
 16. Thesystem of claim 15, wherein to generate the plurality of benchmarkingresults, the processing device is to generate a first plurality ofbenchmarking results representing performance of the computer systemutilizing the first compute resource.
 17. The system of claim 16,wherein the processing device is further to: in response to determiningthat the first plurality of benchmarking results fall within apredefined range, determine a first performance score in view of thefirst plurality of benchmarking results, wherein the first resourceallocation unit comprises the first performance score.
 18. The system ofclaim 13, wherein the first compute resource comprises a firstprocessing resource, and wherein the second compute resource comprises asecond processing resource.
 19. The system of claim 13, wherein thefirst compute resource comprises a first memory resource, and whereinthe second compute resource comprises a second memory resource.
 20. Thesystem of claim 13, wherein the processing device is further to: deploya plurality of containers on the plurality of computer nodes, wherein torun, on the plurality of nodes of the computer system, the benchmarkworkload utilizing the plurality of compute resources, the processingdevice is further to execute the benchmark workload in each of theplurality of containers utilizing the plurality of compute resources.